The present invention relates to techniques for generalizing keyword sets and target audience profiles and, more specifically, using such generalized keyword sets and profiles to enhance online advertising campaigns.
A key success metric for an online advertisement campaign is the number of conversion events (e.g., sale of goods or services, registration, or lead generation) that are attributed to the campaign. Online advertisers are therefore interested in maximizing conversion events given a specific budget. Branded ads and sponsored search ads are considered the two main forms of online advertising. Both forms of online advertising strive to show the most relevant ads to users in order to maximize the ad effectiveness. In branded advertising, advertisers typically specify a profile of the user segment they wish to target, i.e., the target audience. Ads are then shown to users who correspond to the target audience. In sponsored search, advertisers typically bid on a set of keywords for which their ads would be displayed. Ads are displayed when any of the advertiser's bidded keywords are matched based on the underlying context of the users' online actions. For example, in sponsored search advertising associated with search engines, ads are matched when a search query specified by the user matches one of the keywords on which an advertiser has bid.
Large advertisers either have in-house marketing divisions or use professional ad agencies to identify the target audience profiles they should target in their branded ads, as well as to compile lists of keywords and phrases for their sponsored search ads. As stated above, their main objective is to structure the ad campaign so as to maximize the number of conversion events achieved for a given budget. Unfortunately, such conventionally derived profiles and keyword sets have shortcomings which are becoming more problematic as the online advertising market matures.
For example, given that there are relatively few “obvious” keywords relating to a given product or service as compared with the number of potential advertisers, the cost of bidding on the common keywords is becoming prohibitive for even the larger advertisers. This forces advertisers to use undesirably small sets of keywords, and/or to attempt to identify related but less desirable keywords to include in their keyword set, thus potentially reducing the efficacy of their campaigns. Even when costs are not a consideration, finding appropriate keywords is a non-trivial undertaking given that a significant part of queries (e.g., query tail) is not monetized at all.
Crude techniques exist for identifying additional keywords which are related to a particular keyword. For example, in conjunction with returning a set of search results, a search engine might also suggest other related keywords to the user for refining the search. However, because such techniques are typically based on lexical or content analysis, they are not particularly instructive to an advertiser in terms of how effective the additional keywords might be with regard to the intended target audience. In addition, such techniques are not useful in identifying additional keywords having an unexpected or unobvious relationship with the original keyword(s). Similarly, the target audience profiles typically used by advertisers in branded advertising campaigns may be too narrowly or inappropriately focused due to the fact that there are unappreciated correlations in the underlying user population.
In view of the foregoing, there is a need for improved techniques by which advertisers can more effectively target their advertising campaigns.